Abstract
Urban transportation infrastructure, encompassing roads, bridges, and tunnels, is vital for city mobility but remains vulnerable to wear and damage over time. Traditional maintenance methods, which rely on reactive repairs and scheduled inspections, often fall short in preventing sudden failures, resulting in costly disruptions and safety risks. This study examines how artificial intelligence (AI) is revolutionizing infrastructure management through predictive maintenance. By deploying smart sensors and utilizing predictive analytics, AI enables the continuous monitoring of structural health and the proactive identification of potential issues before they escalate into serious failures. The research develops and tests an AI-based predictive maintenance model, which analyzes real-time data from embedded sensors in urban infrastructure to detect anomalies and predict failure patterns. Results indicate that the predictive maintenance model can enhance response times, reduce maintenance costs by 30%, and prevent approximately 92% of unexpected failures. These findings underscore the potential of AI-driven approaches to reduce unplanned disruptions, optimize resource allocation, and extend infrastructure lifespan, ultimately creating safer and more sustainable urban transportation systems. However, challenges in data variability and environmental interference are noted, suggesting areas for future refinement. This study provides a framework for integrating AI in urban infrastructure maintenance, highlighting its potential to transform how cities approach long-term infrastructure health and reliability.
Published Version
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